{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
      "/home/victor/.local/lib/python3.6/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pymc3 as pm\n",
    "import math\n",
    "import json\n",
    "from scipy.stats import gaussian_kde\n",
    "\n",
    "import psycopg2 as psql\n",
    "from psycopg2.extras import RealDictCursor\n",
    "from statsmodels.nonparametric.kde import KDEUnivariate\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "from tools.flight_projection import *\n",
    "from tools.flight_conflict_processor import filter_adsb_gaps\n",
    "from tools.conflict_handling import gamma_angle, get_ac_distance_time, get_dx_dy, get_delta_dst, load_obj, get_delta_dst_t\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    conn = psql.connect(\"dbname='thesisdata' user='postgres' host='localhost' password='postgres'\")\n",
    "except Exception as e:\n",
    "    print(\"Unable to connect to the database.\")\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_dst_kde(kde,hdg,dst_space,pct):\n",
    "    \n",
    "    gs = 50\n",
    "    pdf = kde(np.array([[hdg]*gs,[v for v in dst_space]]))\n",
    "    pdf_zip = list(zip(dst_space,np.cumsum(pdf)/max(np.cumsum(pdf))))\n",
    "    \n",
    "    pct_low = [(d,p) for (d,p) in pdf_zip if p <= 0.5-pct][-1][0]\n",
    "    pct_high = [(d,p) for (d,p) in pdf_zip if p <= 0.5+pct][-1][0]\n",
    "    \n",
    "    return pct_low, pct_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_read_1 = conn.cursor(cursor_factory=RealDictCursor)\n",
    "cur_read_1.execute(\"SELECT ts_1, ts_2, lat_1, lon_1, lat_2, lon_2, hdg_1, hdg_2, spd_1, spd_2 \\\n",
    "                FROM public.converging_flights limit 100;\")\n",
    "batch_1 = cur_read_1.fetchall()\n",
    "\n",
    "cur_read_2 = conn.cursor(cursor_factory=RealDictCursor)\n",
    "cur_read_2.execute(\"SELECT ts_1, ts_2, lat_1, lon_1, lat_2, lon_2, hdg_1, hdg_2, spd_1, spd_2 \\\n",
    "                FROM public.conflicts limit 500;\")\n",
    "batch_2 = cur_read_2.fetchall()\n",
    "\n",
    "batch_1.extend(batch_2)\n",
    "batch = batch_1\n",
    "\n",
    "cur_read_1.close()\n",
    "cur_read_2.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_read = conn.cursor(cursor_factory=RealDictCursor)\n",
    "cur_read.execute(\"SELECT ts_1, ts_2, lat_1, lon_1, lat_2, lon_2, hdg_1, hdg_2, spd_1, spd_2 \\\n",
    "                FROM public.converging_flights limit 500;\")\n",
    "batch = cur_read.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preprocessing flights\n"
     ]
    }
   ],
   "source": [
    "def generate_rand_from_pdf(pdf, x_grid):\n",
    "    cdf = np.cumsum(pdf)\n",
    "    cdf = cdf / cdf[-1]\n",
    "    values = np.random.rand(10000)\n",
    "    value_bins = np.searchsorted(cdf, values)\n",
    "    random_from_cdf = x_grid[value_bins]\n",
    "    return random_from_cdf\n",
    "\n",
    "res_batch = []\n",
    "bin_dict = {}\n",
    "pdf_dict = {}\n",
    "\n",
    "la_time = 1200\n",
    "\n",
    "print('Preprocessing flights')\n",
    "\n",
    "for b in batch:\n",
    "\n",
    "    if len(b['ts_1']) != len(b['ts_2']):\n",
    "        print('Flights do not have same lengths')\n",
    "        continue\n",
    "\n",
    "    f_df = pd.DataFrame.from_dict(b)\n",
    "    if len(f_df) > 0:\n",
    "\n",
    "        dstc_lst = []\n",
    "        dst_lst = []\n",
    "\n",
    "        for i, r in f_df.iterrows():\n",
    "            dstc_lst.append(\n",
    "                get_delta_dst(r['lat_1'], r['lon_1'], r['lat_2'], r['lon_2'],\n",
    "                              r['hdg_1'], r['hdg_2'], r['spd_1'], r['spd_2']))\n",
    "\n",
    "            dst_lst.append(calc_coord_dst((r['lat_1'], r['lon_1']),\n",
    "                                          (r['lat_2'], r['lon_2'])))\n",
    "\n",
    "        f_df['dst_change_i'] = dstc_lst\n",
    "        f_df['dst'] = dst_lst\n",
    "\n",
    "        f_df = f_df.dropna(how='any')\n",
    "        f_df = f_df.reset_index(drop=True)\n",
    "\n",
    "        if len(f_df) > 0:\n",
    "            f_df['dst_change'] = [f_df['dst_change_i'].iloc[0]] * len(f_df)\n",
    "            f_df['dst_diff'] = f_df['dst'].iloc[0] - f_df['dst']\n",
    "            f_df['la_t'] = f_df['ts_1'] - f_df['ts_1'].iloc[0]\n",
    "            \n",
    "            delta_dst_t = []\n",
    "            \n",
    "            for i, rr in f_df.iterrows():\n",
    "                delta_dst_t.append(get_delta_dst_t(rr['lat_1'], rr['lon_1'], rr['lat_2'], rr['lon_2'],\n",
    "                                                   rr['hdg_1'], rr['hdg_2'], rr['spd_1'], rr['spd_2'], rr['la_t']))\n",
    "                                   \n",
    "            f_df['delta_dst_t'] = delta_dst_t\n",
    "                                   \n",
    "            f_df = f_df.dropna(how='any')\n",
    "            f_df = f_df.reset_index(drop=True)\n",
    "            \n",
    "            if len(f_df) > 0:\n",
    "                res_batch.append(f_df.to_dict(orient='list'))\n",
    "\n",
    "\n",
    "# Create a dictionary for each look-ahead time bin with\n",
    "# corresponding heading and distance difference values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating value list\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Creating value list')\n",
    "\n",
    "def distr_func(_d, _dct, _t):\n",
    "    return (_d - _dct) / (_dct * _t)\n",
    "\n",
    "dd_data_raw = []\n",
    "dd_data_raw_100 = []\n",
    "dd_data_raw_400 = []\n",
    "dd_data_raw_800 = []\n",
    "dd_data_raw_1000 = []\n",
    "dd_data_raw_x = []\n",
    "ts_l = []\n",
    "\n",
    "for f in res_batch:\n",
    "\n",
    "    dstd_lst = f['dst_diff']\n",
    "    dst_change = f['dst_change']\n",
    "    ts_lst = f['la_t']\n",
    "    delta_dst_t = f['delta_dst_t']\n",
    "\n",
    "#     dd_data_raw.extend([d / (dc * t) for d, dc, t in\n",
    "#                         zip(dstd_lst, dst_change, ts_lst) if dc*t != 0])\n",
    "    \n",
    "    dd_data_raw_100.extend([distr_func(d, dct, t) for d, dct, t in\n",
    "                        zip(dstd_lst, delta_dst_t, ts_lst) if dct != 0 and 200 < t < 400])\n",
    "    dd_data_raw_400.extend([distr_func(d, dct, t) for d, dct, t in\n",
    "                        zip(dstd_lst, delta_dst_t, ts_lst) if dct != 0 and 400 < t < 600])\n",
    "    dd_data_raw_800.extend([distr_func(d, dct, t) for d, dct, t in\n",
    "                        zip(dstd_lst, delta_dst_t, ts_lst) if dct != 0 and 600 < t < 800])\n",
    "    dd_data_raw_1000.extend([distr_func(d, dct, t) for d, dct, t in\n",
    "                        zip(dstd_lst, delta_dst_t, ts_lst) if dct != 0 and 800 < t < 1000])\n",
    "    \n",
    "    dd_data_raw_x.extend([distr_func(d, dct, t) for d, dct, t in\n",
    "                        zip(dstd_lst, delta_dst_t, ts_lst) if dct != 0])\n",
    "    ts_l.extend([t for d, dct, t in\n",
    "                        zip(dstd_lst, delta_dst_t, ts_lst) if dct != 0])\n",
    "\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "cs = ['r','b','k','g','m']\n",
    "label_set = ['Look-ahead times: 200 < t < 400 (s)', 'Look-ahead times: 400 < t < 600 (s)', \n",
    "          'Look-ahead times: 600 < t < 800 (s)', 'Look-ahead times: 800 < t < 1000 (s)', 'Look-ahead times: all']\n",
    "flen = 20000\n",
    "for i, dd_data_raw in enumerate([dd_data_raw_100[:flen], dd_data_raw_400[:flen], \n",
    "                                 dd_data_raw_800[:flen], dd_data_raw_1000[:flen], dd_data_raw_x[:flen]]):\n",
    "#     dd_data_raw = \n",
    "    dd_data_p = np.percentile(dd_data_raw, 99.5)\n",
    "    dd_data_filt = [v for v in dd_data_raw if abs(v) < dd_data_p]\n",
    "\n",
    "    x_grid = np.linspace(min(dd_data_filt), max(dd_data_filt), 1000)\n",
    "    kde = gaussian_kde(dd_data_filt)#, bw_method=0.1)\n",
    "    kdepdf = kde.evaluate(x_grid)\n",
    "    random_from_kde = generate_rand_from_pdf(kdepdf, x_grid)\n",
    "\n",
    "    \n",
    "#     plt.hist(dd_data_filt,density=False, bins=400)\n",
    "    # plt.hist(random_from_kde, 500, normed=True, alpha=0.5, label='hist')\n",
    "    plt.plot(x_grid, kdepdf/max(kdepdf), color=cs[i], linewidth=2, label=label_set[i])\n",
    "\n",
    "# plt.xlim(-1,1)\n",
    "dd_data_p80 = np.percentile(dd_data_raw, 97)\n",
    "plt.xlim(-dd_data_p80, dd_data_p80)\n",
    "plt.xlabel('$D_{ac1/ac2_{error}}$')\n",
    "plt.ylabel('Probability Density')\n",
    "plt.title(\"Distribution of $D_{ac1/ac2_{error}}$ for different look-ahead times\")\n",
    "plt.legend(loc='best', prop={'size': 10})\n",
    "plt.show()\n",
    "\n",
    "# Create final dictionary\n",
    "\n",
    "pct_dict = {}\n",
    "cdf_list = list(zip(x_grid, np.cumsum(kdepdf)/np.cumsum(kdepdf)[-1]))\n",
    "for p in range(1, 100):\n",
    "    pct_dict[str(p)] = [v[0] for v in cdf_list if v[1] <= p/100][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-100, 100)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,30))\n",
    "plt.scatter(ts_l, dd_data_raw_x)\n",
    "plt.xlim(0,1200)\n",
    "plt.ylim(-100,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "res_batch = []\n",
    "\n",
    "for b in batch:\n",
    "    \n",
    "#     print(b.keys())\n",
    "    filter_flag = 0\n",
    "\n",
    "    if filter_flag == 1:\n",
    "        ix = filter_adsb_gaps(b['ts_1'])\n",
    "        if not ix:\n",
    "            ix = (0, -1)\n",
    "    else:\n",
    "        ix = (0, -1)\n",
    "\n",
    "    for k in b.keys():\n",
    "        if '_1' in k:\n",
    "            b[k] = b[k][ix[0]:ix[1]]\n",
    "        if '_2' in k:\n",
    "            b[k] = b[k][ix[0]:ix[1]]\n",
    "\n",
    "    if len(b['ts_1']) != len(b['ts_2']):\n",
    "        print('Flights do not have same lengths')\n",
    "        continue\n",
    "\n",
    "    f_df = pd.DataFrame.from_dict(b)\n",
    "    if len(f_df) > 0:\n",
    "        f_df['spd_diff'] = f_df['spd_1'] - f_df['spd_2']\n",
    "        \n",
    "#         f_df = f_df.drop_duplicates(['lat_1', 'lon_1'])\n",
    "#         f_df = f_df.drop_duplicates(['lat_2', 'lon_2'])\n",
    "#         f_df = f_df.reset_index(drop=True)\n",
    "\n",
    "        dstc_lst = []\n",
    "        hdg_diff_lst = []\n",
    "        dst_lst = []\n",
    "\n",
    "        for i,r in f_df.iterrows():\n",
    "            dstc_lst.append(get_delta_dst(r['lat_1'], r['lon_1'],r['lat_2'],r['lon_2'],r['hdg_1'], r['hdg_2'], r['spd_1'], r['spd_2']))\n",
    "\n",
    "            _g, _a, _b = gamma_angle(r['lat_1'], r['lon_1'],r['lat_2'], \n",
    "                                          r['lon_2'],r['hdg_1'], r['hdg_2'])\n",
    "            hdg_diff_lst.append(_g)\n",
    "\n",
    "            dst_lst.append(calc_coord_dst((r['lat_1'], r['lon_1']),\n",
    "                                      (r['lat_2'], r['lon_2'])))\n",
    "\n",
    "        f_df['dst_change_i'] = dstc_lst\n",
    "        f_df['hdg_diff_i'] = hdg_diff_lst\n",
    "        f_df['dst'] = dst_lst\n",
    "\n",
    "        f_df = f_df.dropna(how='any')\n",
    "        f_df = f_df.reset_index(drop=True)\n",
    "\n",
    "        if len(f_df) > 0:\n",
    "            f_df['dst_change'] = [f_df['dst_change_i'].iloc[0]]*len(f_df)\n",
    "            f_df['hdg_diff'] = [f_df['hdg_diff_i'].iloc[0]]*len(f_df)\n",
    "            f_df['dst_diff'] = f_df['dst'] - f_df['dst'].iloc[0]\n",
    "            f_df['la_t'] = f_df['ts_1'] - f_df['ts_1'].iloc[0]\n",
    "\n",
    "    #         bi = f_df.to_dict(type='list')\n",
    "            res_batch.append(f_df.to_dict(orient='list'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_dict = {}\n",
    "la_time = 1200\n",
    "bin_sec = 20\n",
    "\n",
    "for f in res_batch:\n",
    "        \n",
    "    hdgd_lst = f['hdg_diff']\n",
    "    hdgd_lst_i = f['hdg_diff_i']\n",
    "    dstd_lst = f['dst_diff']\n",
    "    dst_change = f['dst_change']\n",
    "    dst_change_i = f['dst_change_i']\n",
    "    spd_lst = f['spd_diff']\n",
    "    ts_lst = f['la_t']\n",
    "\n",
    "    for b in range(int(la_time/bin_sec)):\n",
    "        bmin = b*bin_sec\n",
    "        bmax = (b+1)*bin_sec\n",
    "        if str(bmax) not in list(bin_dict.keys()):\n",
    "            bin_dict[str(bmax)] = []\n",
    "\n",
    "        bin_dict[str(bmax)].extend([(h,d,dc,spd,t) for h,d,dc,spd,t in \n",
    "                                    zip(hdgd_lst_i, dstd_lst, dst_change, spd_lst, ts_lst) if t >= bmin and t <= bmax])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def generate_rand_from_pdf(pdf, x_grid):\n",
    "    cdf = np.cumsum(pdf)\n",
    "    cdf = cdf / cdf[-1]\n",
    "    values = np.random.rand(10000)\n",
    "    value_bins = np.searchsorted(cdf, values)\n",
    "    random_from_cdf = x_grid[value_bins]\n",
    "    return random_from_cdf\n",
    "\n",
    "\n",
    "x_data_raw = []\n",
    "for la_ti in [i*20 for i in range(1,61)]:\n",
    "    x_data_raw.extend([v[1]/(v[4]*v[2]) for v in bin_dict['%d' % la_ti] if v[4]>0])\n",
    "\n",
    "x_data_p = np.percentile(x_data_raw,99.5)\n",
    "x_data_filt = [v for v in x_data_raw if abs(v) < x_data_p][:10000]\n",
    "print(len(x_data_filt))\n",
    "\n",
    "x_grid = np.linspace(min(x_data_filt), max(x_data_filt), 1000)\n",
    "kde = gaussian_kde(x_data_filt, bw_method=0.1)\n",
    "kdepdf = kde.evaluate(x_grid)\n",
    "random_from_kde = generate_rand_from_pdf(kdepdf, x_grid)\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "plt.hist(x_data_filt,normed=True, bins=400)\n",
    "plt.hist(random_from_kde, 500, normed=True, alpha=0.5, label='hist')\n",
    "plt.plot(x_grid, kdepdf, color='r', alpha=0.5, label='kde')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pct_dict = {}\n",
    "cdf_list = list(zip(x_grid,np.cumsum(kdepdf)/np.cumsum(kdepdf)[-1]))\n",
    "for p in range(1, 100):\n",
    "    pct_dict[str(p)] = [v[0] for v in cdf_list if v[1] <= p/100][-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pct_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_pct_dict = load_obj(\"../tools/kde_dictionary.pkl\")\n",
    "_pct_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "pdf_dict = {}\n",
    "hdg_dst_dict = {}\n",
    "la_ti = 1200\n",
    "gsize=50\n",
    "\n",
    "for la_ti in [i*20 for i in range(1,61)]:\n",
    "    dstc_data_raw = [v[1] for v in bin_dict['%d' % la_ti]]\n",
    "    dst_data_raw = [v[0] for v in bin_dict['%d' % la_ti]]\n",
    "    if len(dstc_data_raw) == len(dst_data_raw):\n",
    "        \n",
    "        print('Creating kde for la %d' % la_ti)\n",
    "\n",
    "        dat = [v for v in list(zip(dstc_data_raw, dst_data_raw)) if\n",
    "               all(~np.isnan(vi) for vi in v)]\n",
    "\n",
    "        hl, dl = zip(*dat)\n",
    "        dstc_data_raw, dst_data_raw = (list(hl), list(dl))\n",
    "        data = np.vstack([dstc_data_raw, dst_data_raw])\n",
    "\n",
    "        kde = gaussian_kde(data, bw_method=0.1)\n",
    "\n",
    "        dst_space = np.linspace(min(dst_data_raw), max(dst_data_raw), gsize)\n",
    "\n",
    "        pdf_dict['%d' % la_ti] = kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf_dict = {}\n",
    "hdg_dst_dict = {}\n",
    "la_ti = 1200\n",
    "gsize=200j\n",
    "\n",
    "dstc_data_raw = [v[1] for v in bin_dict['%d' % la_ti]]\n",
    "dst_data_raw = [v[0] for v in bin_dict['%d' % la_ti]]\n",
    "if len(dstc_data_raw) == len(dst_data_raw):\n",
    "\n",
    "    print('Creating kde for la %d' % la_ti)\n",
    "\n",
    "    dat = [v for v in list(zip(dstc_data_raw, dst_data_raw)) if\n",
    "           all(~np.isnan(vi) for vi in v)]\n",
    "\n",
    "    hl, dl = zip(*dat)\n",
    "    dstc_data_raw, dst_data_raw = (list(hl), list(dl))\n",
    "    data = np.vstack([dst_data_raw, dstc_data_raw])\n",
    "\n",
    "    kde = gaussian_kde(data, bw_method=0.2)\n",
    "\n",
    "#     dst_space = np.linspace(min(dst_data_raw), max(dst_data_raw), gsize)\n",
    "\n",
    "    pdf_dict['%d' % la_ti] = kde\n",
    "\n",
    "X, Y = np.mgrid[min(dst_data_raw):max(dst_data_raw):gsize, min(dstc_data_raw):max(dstc_data_raw):gsize]\n",
    "grid = np.vstack([X.ravel(),Y.ravel()])\n",
    "pdf = kde(grid)\n",
    "f = np.reshape(pdf.T, X.shape)\n",
    "\n",
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.gca()\n",
    "# ax.set_xlim(min(dst_data_raw), max(dst_data_raw))\n",
    "# ax.set_ylim(min(dstc_data_raw), max(dstc_data_raw))\n",
    "# Contourf plot\n",
    "\n",
    "cfset = ax.contourf(X, Y, f, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X, Y = np.mgrid[min(hdg_data_raw):max(hdg_data_raw):gsize, min(dst_data_raw):max(dst_data_raw):gsize]\n",
    "# X, Y = np.mgrid[10, min(dst_data_raw):max(dst_data_raw):gsize]\n",
    "# grid = np.vstack([X.ravel(),Y.ravel()])\n",
    "dst_space = np.linspace(min(dst_data_raw),max(dst_data_raw),gsize)\n",
    "pdf = kde(np.array([[50]*gsize,[v for v in dst_space]]))\n",
    "# plt.plot(dst_space,pdf)\n",
    "np.percentile(pdf,60)\n",
    "# plt.plot(dst_space,np.cumsum(pdf)/max(np.cumsum(pdf)))\n",
    "# f = np.reshape(pdf.T, X.shape)\n",
    "#     pdf_dict[str(la_ti)] = kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(20,20))\n",
    "ax = fig.gca()\n",
    "ax.set_ylim(min(dst_data_raw), max(dst_data_raw))\n",
    "ax.set_xlim(min(hdg_data_raw), max(hdg_data_raw))\n",
    "# Contourf plot\n",
    "\n",
    "cfset = ax.contourf(X, Y, f, cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_batch_f = [ri for ri in res_batch if any(v < -25000 for v in ri['dst_diff'])]\n",
    "print(len(res_batch_f))\n",
    "print(len(res_batch))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from conf.config import ipz_range, knts_ms_ratio\n",
    "r = res_batch[25]\n",
    "\n",
    "print(r['hdg_1'][0])\n",
    "print(r['hdg_2'][0])\n",
    "print(r['dst'][0])\n",
    "# print(r['lat_1'][0], r['lon_1'][0],r['lat_2'][0], r['lon_2'][0])\n",
    "print(calc_compass_bearing((r['lat_1'][0], r['lon_1'][0]),(r['lat_2'][0], r['lon_2'][0])))\n",
    "# gamma_angle(r['lat_1'][0], r['lon_1'][0],r['lat_2'][0], r['lon_2'][0], r['hdg_1'][0], r['hdg_2'][0])\n",
    "\n",
    "gm, a, b = gamma_angle(r['lat_1'][0], r['lon_1'][0],r['lat_2'][0], r['lon_2'][0], r['hdg_1'][0], r['hdg_2'][0])\n",
    "print(a,b)\n",
    "dx1, dx2, dy1, dy2 = get_dx_dy(a, b, r['spd_1'][0], r['spd_2'][0])\n",
    "print(dx1, dx2, dy1, dy2)\n",
    "print(get_ac_distance_time(r['dst'][0],dx1, dx2, dy1, dy2,1) - r['dst'][0])\n",
    "print(get_ac_distance_time(r['dst'][0],dx1, dx2, dy1, dy2,(r['ts_1'][1] - r['ts_1'][0])) - r['dst'][0])\n",
    "print((r['dst'][0] - r['dst'][1])/(r['ts_1'][0] - r['ts_1'][1]))\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.scatter(r['lon_1'], r['lat_1'])\n",
    "plt.scatter(r['lon_2'], r['lat_2'])\n",
    "plt.scatter(r['lon_1'][0], r['lat_1'][0],c='g')\n",
    "plt.scatter(r['lon_2'][0], r['lat_2'][0],c='g')\n",
    "plt.scatter(r['lon_1'][-1], r['lat_1'][-1],c='r')\n",
    "plt.scatter(r['lon_2'][-1], r['lat_2'][-1],c='r')\n",
    "plt.axis('scaled')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ri = res_batch[25]\n",
    "\n",
    "coord_dst = []\n",
    "for i in range(len(ri['lat_1'])):\n",
    "    coord_dst.append(calc_coord_dst((ri['lat_1'][i], ri['lon_1'][i]),\n",
    "                   (ri['lat_2'][i], ri['lon_2'][i])))\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(ri['la_t'], ri['hdg_diff_i'])\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(ri['la_t'], ri['dst_diff'])\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(ri['la_t'], ri['dst_change_i'])\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(ri['la_t'], coord_dst)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.scatter(ri['lon_1'], ri['lat_1'])\n",
    "plt.scatter(ri['lon_2'], ri['lat_2'])\n",
    "plt.scatter(ri['lon_1'][0], ri['lat_1'][0],c='g')\n",
    "plt.scatter(ri['lon_2'][0], ri['lat_2'][0],c='g')\n",
    "plt.scatter(ri['lon_1'][-1], ri['lat_1'][-1],c='r')\n",
    "plt.scatter(ri['lon_2'][-1], ri['lat_2'][-1],c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_read.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = np.mgrid[min(hdg_data_raw):max(hdg_data_raw):gsize, min(dst_data_raw):max(dst_data_raw):10j]\n",
    "grid = np.vstack([X.ravel(),Y.ravel()])\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "t = time.time()\n",
    "sample_dst_kde(kde, h, dst_space, 0.2)\n",
    "print(time.time() - t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gsize=200\n",
    "kde = pdf_dict['20']\n",
    "dst_space = np.linspace(min(dst_data_raw),max(dst_data_raw),gsize)\n",
    "pdf = kde(np.array([[10]*gsize,[v for v in dst_space]]))\n",
    "\n",
    "plt.plot(dst_space,pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "950.4735661763561"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ac_distance_time(1000, 3, -2, 2, 5, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "600*(get_ac_distance_time(1000, 2, 3, 2.5, 3.5, 1) - 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = 1000\n",
    "dx1 = 2\n",
    "dx2 = 3\n",
    "dy1 = 2.5\n",
    "dy2 = 3.5\n",
    "\n",
    "d = s\n",
    "dend = 950.4735\n",
    "X = (dx1 + dx2)\n",
    "Y = (dy1 - dy2)\n",
    "Z = d**2 + 2*X*Y - 2*d*Y + Y**2\n",
    "\n",
    "(2*d*X) - np.sqrt((4 * d**2 * X**2) - (4 * X**2 * (Z - dend))) / (2 * X**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt((4 * d**2 * X**2) - (4 * X**2 * (Z - dend)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-104052.65000000596"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(4 * d**2 * X**2) - (4 * X**2 * (Z - dend))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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